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Imperfect hydroxyapatite bioceramics derived from golden pomfret have enhanced osteogenic properties
Abstract Imperfect hydroxyapatite (IHA) bioceramics, which contain defects such as calcium deficiency, carbonate substitution, and metal cation substitution, exhibit improved osteogenic properties. In this study, we used a two-step calcination-hydrothermal process to manufacture two types of golden pomfret bone-derived imperfect hydroxyapatite bioceramics (G-IHA): carbonated calcium-deficient hydroxyapatite (CD-IHA) and carbonated hydroxyapatite (C-IHA). Their composition, surface morphology, zeta potential, degradation capacity, mineralization and osteogenic properties were systematically investigated. The results revealed that G-IHA with a higher defect content, including A-type carbonate substitution and Ca vacancies, had negatively charged surface. As a result, G-IHA surfaces are more favourable to ion exchange and interaction with cations (e.g., Na+, Ca2+) in the microenvironment, which results in improved degradation and mineralization. Specifically, after 28 days of degradation, G-IHA showed significantly higher weight losses (CD-IHA and C-IHA were 17% and 13%, respectively) than commercial hydroxyapatite (CHA; 7%). In addition, G-IHA have a higher better bone-like apatite formation ability, and a higher degree of osteogenic differentiation than CHA. Notably, carbonated calcium-deficient imperfect hydroxyapatite (CD-IHA) exhibited the highest bioactivity and osteogenic capacity as evidenced by its increased alkaline phosphatase activity and improved bone matrix mineralization capacity. In conclusion, this study revealed that imperfect hydroxyapatite bioceramics derived from golden pomfret bone have the potential to enhance osteogenic properties and be employed in clinical settings as bone substitute materials.
Genetic diversity, population structure, and cannabinoid variation in feral Cannabis sativa germplasm from the United States
Comparison of AI chatbot predicted and realworld survival outcomes in hepatocellular carcinoma
PD1 × VEGF blocking bispecifics for cancer draw big backing
5hmC enhances PARP trapping and restores PARP inhibitor sensitivity in chemoresistant BRCA1/2-deficient cells
Impact of HLA evolutionary divergence and donor-recipient molecular mismatches on antibody-mediated rejection of kidney allografts
GAUDI: interpretable multi-omics integration with UMAP embeddings and density-based clustering
Bayesian Meta‐Learning for Few‐Shot Reaction Outcome Prediction of Asymmetric Hydrogenation of Olefins
Abstract Recent years have witnessed the increasing application of machine learning (ML) in chemical reaction development. These ML methods, in general, require huge training set examples. The published literature has large amounts of data, but there are modelling challenges due to the sparse nature of these datasets. Herein, we report a meta‐learning workflow that can utilize the literature‐mined data and return accurate predictions with limited data. A literature dataset comprising of over 12 000 transition metal catalyzed asymmetric hydrogenation of olefins (AHO) is chosen to demonstrate the utility of our protocol. A meta‐model is trained in a binary classification setting to identify highly enantioselective AHO reactions. Two Bayesian meta‐learning approaches are considered, namely, deep kernel transfer (DKT) and adaptive deep kernel fitting (ADKF). Both these methods returned better predictions compared to prototypical network, which is another popular meta‐learning approach. Single‐task methods, such as random forest, graph neural network, and deep kernel learning, performed worse than meta‐learning methods even when trained on full training data. Additionally, we propose another meta‐learning approach called ADKF‐prior that is shown to further improve the performance in low‐data settings. The generalizability of our meta‐model is also evaluated on substrate‐ and time‐based splits. Our meta‐learning workflow can be utilized to build a pretrained meta‐model for any reaction of interest, which can then be useful to predict the outcome of new but related reactions in a few‐shot manners.
Isolation, identification, and evaluation of the antioxidant properties of lactic acid bacteria strains isolated from meat environment
Oxidative stress is a condition in which the body loses balance between the production of free radicals and the body’s ability to neutralize them. The role of antioxidants is to protect cells and tissues from the harmful effects of excessive amounts of free oxygen radicals. Lactic acid bacteria (LAB) can exhibit significant antioxidant properties which is the subject of research by many scientists. The aim of the work was isolation, phenotypic and genotypic identification, and evaluation of the antioxidant activity of twenty-one bacterial strains from raw fermented beef hams and the environment of a meat factory. The bacteria were screened in vitro by investigating their DPPH (1,1-diphenyl-2-picrylhydrazyl) and ABTS (2,2′-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid)) free radical scavenging activity, superoxide anion tests, hydroxyl radical resistance, superoxide dismutase and catalase activity, and hydrogen peroxide resistance. As a result of the conducted research, 21 bacterial strains were isolated. They were assigned to Lactiplantibacillus plantarum (14), Lactiplantibacillus pentosus (3), Lactiplantibacillus argentoratensis (2), Lacticaseibacillus paracasei (1), and Pediococcus pentosaceus (1). The strains were compared with each other and some of them were able to scavenge free radicals DPPH (1.08–36.91%), ABTS (11.24–51.05%), and superoxide anions (3.04–96.70%). Furthermore, resistance to high concentrations of hydrogen peroxide (0.4–1.0 mM H2O2) and hydroxyl radicals (25.90–99.22%) has been demonstrated. Some strains produced superoxide dismutase, while none of them produced catalase. The findings indicated that some LAB strains could be promising starter candidates with antioxidant properties.